![WechatIMG2968](https://user-images.githubusercontent.com/7423248/128490653-f55fefa8-f944-4617-96b8-5cc94f14f8f6.png)

[![MIT License](https://img.shields.io/badge/license-MIT-blue.svg?style=flat)](http://choosealicense.com/licenses/mit/)
> This repository is forked from [Real-Time-Voice-Cloning](https://github.com/CorentinJ/Real-Time-Voice-Cloning) which only support English.

> English | [中文](README-CN.md) 

## Features
🌍 **Chinese** supported mandarin and tested with multiple datasets: aidatatang_200zh, SLR68

🤩 **PyTorch** worked for pytorch, tested in version of 1.9.0(latest in August 2021), with GPU Tesla T4 and GTX 2060

🌍 **Windows + Linux** tested in both Windows OS and linux OS after fixing nits 

🤩 **Easy & Awesome** effect with only newly-trained synthesizer, by reusing the pretrained encoder/vocoder


### [DEMO VIDEO](https://www.bilibili.com/video/BV1sA411P7wM/)

## Quick Start

### 1. Install Requirements
> Follow the original repo to test if you got all environment ready.
**Python 3.7 or higher ** is needed to run the toolbox.

* Install [PyTorch](https://pytorch.org/get-started/locally/).
* Install [ffmpeg](https://ffmpeg.org/download.html#get-packages).
* Run `pip install -r requirements.txt` to install the remaining necessary packages.

### 2. Reuse the pretrained encoder/vocoder
* Download the following models and extract to the root directory of this project. Don't use the synthesizer
https://github.com/CorentinJ/Real-Time-Voice-Cloning/wiki/Pretrained-models
> Note that we need to specify the newly trained synthesizer model, since the original model is incompatible with the Chinese sympols. It means the demo_cli is not working at this moment.
### 3. Train synthesizer with aidatatang_200zh
* Download aidatatang_200zh dataset and unzip: make sure you can access all .wav in *train* folder
* Preprocess with the audios and the mel spectrograms:
`python synthesizer_preprocess_audio.py <datasets_root>`
Allow parameter `--dataset {dataset}` to support adatatang_200zh, SLR68
* Preprocess the embeddings:
`python synthesizer_preprocess_embeds.py <datasets_root>/SV2TTS/synthesizer`

* Train the synthesizer:
`python synthesizer_train.py mandarin <datasets_root>/SV2TTS/synthesizer`

* Go to next step when you see attention line show and loss meet your need in training folder *synthesizer/saved_models/*. 
> FYI, my attention came after 18k steps and loss became lower than 0.4 after 50k steps.
![attention_step_20500_sample_1](https://user-images.githubusercontent.com/7423248/128587252-f669f05a-f411-4811-8784-222156ea5e9d.png)
![step-135500-mel-spectrogram_sample_1](https://user-images.githubusercontent.com/7423248/128587255-4945faa0-5517-46ea-b173-928eff999330.png)

### 4. Launch the Toolbox
You can then try the toolbox:

`python demo_toolbox.py -d <datasets_root>`  
or  
`python demo_toolbox.py`  

## TODO
- [x] Add demo video
- [X] Add support for more dataset
- [ ] Upload pretrained model
- 🙏 Welcome to add more